十、文档扫描

需扫描的文档

scan.py

import numpy as np
import cv2


# 获取四个点
def get_point(pts):
    rect = np.zeros((4, 2), dtype="float32")

    # 按顺序计算四个坐标：左上，右上，右下，左下
    s = pts.sum(axis=1)

    # 左上，右下
    rect[0] = pts[np.argmin(s)]
    rect[2] = pts[np.argmax(s)]

    # 右上，左下
    diff = np.diff(pts, axis=1)
    rect[1] = pts[np.argmin(diff)]
    rect[3] = pts[np.argmax(diff)]
    return rect


def point_transform(image, pts):
    # 获取输入坐标点
    rect = get_point(pts)
    (tl, tr, br, bl) = rect

    # 计算输入的w、h
    widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
    widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
    maxWidth = max(int(widthA), int(widthB))

    heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
    heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
    maxHeight = max(int(heightA), int(heightB))

    # 变换后的对应坐标位置
    dst = np.array([[0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]], dtype="float32")

    # 计算变换矩阵
    M = cv2.getPerspectiveTransform(rect, dst)
    warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
    return warped


def resize(image, width=None, height=None, inter=cv2.INTER_AREA):
    dim = None
    (h, w) = image.shape[:2]
    if width is None and height is None:
        return image
    if width is None:
        r = height / float(h)
        dim = (int(w * r), height)
    else:
        r = width / float(w)
        dim = (width, int(h * r))
    resized = cv2.resize(image, dim, interpolation=inter)
    return resized


# 读取文档
image = cv2.imread("images/document_1.jpg")
ratio = image.shape[0] / 500.0
image_copy = image.copy()

image = resize(image_copy, height=500)

# 灰度处理
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# 高斯处理
gray = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(gray, 50, 150)

# 预处理完的结果
cv2.imshow("Image", image)
cv2.imshow("Edged", edged)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 轮廓检测
screenCnt = None
cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[1]
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]
for c in cnts:
    # 计算近似轮廓
    peri = cv2.arcLength(c, True)
    # 参数说明：输入的点集、准确度（从原始轮廓到近似轮廓的最大距离）、是否封闭
    approx = cv2.approxPolyDP(c, 0.02 * peri, True)

    if len(approx) == 4:
        screenCnt = approx
        break

# 绘制轮廓
cv2.drawContours(image, [screenCnt], -1, (2, 255, 0), 2)
cv2.imshow("image", image)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 透视变换（二维源图 ---> 三维 ---> 二维）
warped = point_transform(image_copy, screenCnt.reshape(4, 2) * ratio)

# 二值处理
warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
ref = cv2.threshold(warped, 100, 255, cv2.THRESH_BINARY)[1]
cv2.imwrite("scan.jpg", ref)

# 输出结果
cv2.imshow("origin", resize(image_copy, height=500))
cv2.imshow("result", resize(ref, height=500))
cv2.waitKey(0)
cv2.destroyAllWindows()